Most of the recent works [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]} on the text-to-image synthesis problem are based on GANs. We generalize these approaches to a unified framework, as shown in Figure REF a. The framework is extended from the GANs with the auxiliary information \(e\) , which is encoded from the input caption \(t\)  by a pre-trained text encoder \(g\) . Then both the generator \(G\)  and discriminator \(D\)  are conditioned on this textual condition \(e\) . The training process is similar to the standard GANs with the following objective:
\(\begin{adjustbox}{max width=215pt}\underset{G}{\min }\:\underset{D}{\max } \!\! \underset{\:\:x \sim P_r}{\mathbb {E}}\![\log (D(x, e))]\!+\!\!\!\underset{\:z \sim P_z}{\mathbb {E}} [\log (1\!-\!D(G(z,e),e))]\end{adjustbox}\) 
